Industry Spotlight: Manufacturing & Big Data

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68% of manufacturers are currently investing in big data analytics in the next twelve months and 67% are moving forward with these investments even as they cut in other areas. Mr. Louis Columbus of Forbes breaks down for us a recent analytics/IoT Honeywell survey of 200 North American manufacturing executives.

According to the survey, 46% of respondents no longer see big data analytics as “optional.”  Instead, the benefits of big data analytics are clear to many in the industry:

  • Equipment performance.  Unscheduled downtime was sited as a top threat to revenue.  51% of respondents agree that the combination of the Industrial Internet of Things (IIoT) and big data analytics will help to predict equipment downtime, maintenance needs and breaks/repairs
  •  Supply chain management. 46% of the executives surveyed agree that big data analytics will help with supply chain performance by allowing them to better plan for and use resources more efficiently
  • Safety. 47% of respondents agree that access to better equipment and operational data through big data analytics could help detect possible safety issues
Big Data Benefits - Forbes
Source: Forbes.com

Mr. Columbus reports most executives are fairly confident on the analytics path they are on. In fact, 65% report being on the “right track” or “above the curve” when it comes to their use of big data analytics. In summary, the results of this survey show that big data analytics will be key to allowing those in the manufacturing industry to improve revenue and operations for their companies.

Modernize Your Data Warehouse

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Entrepreneur reports that by 2020 every person online will create 1.7 megabytes of new data every second, and according to a study on  Computerworld, by that same year $72 billion will be spent on big data hardware, software and professional services. Data is growing and its coming in many forms both structured and unstructured, and companies are looking for the tools to help them manage and utilize it.

Big data can provide a more complete picture of your customer, marketplace or product/service. But before you can take advantage of big data, you must first have the right systems and tools in place.  Now is the time to consider your own infrastructure to make sure the right workloads are working on the right technologies.  In fact, it may be time for your company to take a deep look into how your data warehouse architecture can be modernized, so you are ready to move ahead with big data analytics projects.

Modernizing your data warehouse can increase your ability to deploy new workloads and handle new data sources.  You can also simplify your operations. Modern appliances can be integrated with your data warehouse, filling service gaps and making deployment and management of data easier.  Finally, a whole new set of tools for big data analysis are available. These analytics tools can integrate into your system allowing you to crunch massive amounts of data to form actionable insights.

So how do you get started on the right path? A trusted partner can help you develop a plan for the changes needed to modernize your data warehouse architecture and can help identify the right big data analytics tools for your company. Evolving Solutions takes the time to fully assess your data and business objectives, develop customized software solutions based on your needs, and we provide ongoing support.  We take the time to understand your goals and help you to accomplish them. Contact us today to start discussing how to modernize your data warehouse.

Industry Spotlight: Banking & Big Data

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Big data use has been expanding across a variety of industries. Today let’s focus on the banking industry and how it is using big data.

Business Insider reports that some of the biggest banks in the world hope to use the information found on social media channels like Facebook, Twitter and Instagram as part of a customer’s credit history or in place of one when none is available.  Of course, developing a system to handle this type of data does not only bring technology and system challenges but also regulation and privacy challenges. Still, many feel tapping into social media would allow the industry to get amore complete picture of the consumer’s financial status and credit.

Bernard Marr spotlights Citibank’s use of big data in a Forbes article.  Citibank is taking a “data-led” approach to its decision making and using big data analytics across areas such as customer retention and acquisition, customer service and compliance and fraud. Citibank has over 200 million customer accounts and operates in hundreds of countries which all equates to a lot of data to handle and store. Adopting big data tools and processes has also helped Citibank to utilize data better and manage and store data more effectively. Mr. Marr writes, “At Citi, model testing allows for a holistic understanding of innovative use cases by deconstructing data at its most granular level as well as synthesizing structured and unstructured data sources.”

The banking industry is making inroads with big data to better serve its customers. It continues to look for solutions that strike a balance between regulations and privacy and solutions that support the vast amounts of transactional and customer data it already has with new data sources.

Cognitive Computing: Improving X-Rays

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“What if your X-ray could predict a potential disease months in advance,” writes Laura Lorenzetti for Fortune. This past summer IBM Watson Health created a new partnership with leading medical providers and imaging tech companies to see if cognitive computing can take medical imaging one step further to actually predict the chance of diseases like cancer and heart failure.

Ms. Lorenzetti’s article points out that much of the data gathered from an x-ray or MRI is “unstructured.” It can be difficult for computers to connect the information to patient records in a meaningful way. IBM Watson Health is trying to change that and utilizing its power to connect unstructured data with its massive databases of patient medical history.  Ms. Lorenzetti writes, “the goal is to provide new offerings across various medical environments (a hospital ER or an everyday physician’s office) that can connect systems (medical records, picture archiving, lab results) and deliver cognitive insights to doctors on the spot for better diagnoses.”

One example from the article is the use of mammograms. Not only could Watson connect the image results to the patient’s medical history but it could also “cross-reference against the similar patients within the Watson database.”  These connections could improve a doctor’s  ability to identify early warning signs or risks.

Another example is the use of cognitive computing to help doctors predict which patients are more likely to have a heart attack after reporting chest pain.  Ms. Lorenzetti reports that 2% of patients who visit an ER with chest pain have the early signs of a heart attack missed. By connecting the data dots IBM Watson Health could help doctors identify these signs better.

IBM Watson Health’s shear power to process unstructured data such as medical imaging while also consuming vast amounts of patient data allows for it to draw cognitive insights that will one day improve patient diagnoses and treatments.

Industries Exceling with Big Data

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“Two and a half quintillion bytes or 2,500,000,000,000,000,000 bytes. That’s how much data humanity generates every single day. And the amount is increasing; we’ve created 90% of the world’s data in the last two years alone,” writes Michael Belfiore for IBM’s Watson Blog.

Many businesses struggle with this ever increasing data, especially growing unstructured data, but other businesses in an assortment of industries are succeeding by using cognitive computing technology to turn big data into big insights.  Mr. Beliofe provides a summary of several industries succeeding with big data:

Telecommunications – cognitive computing tools are being used to index documents, images and manuals giving call center agents access to more actionable data to better solve customer issues. Every second saved in call time is $1 in cost savings.

Finance – an auto finance firm is using big data to develop better customer insights that not only allow them to serve customers better but also to improve data security.

Healthcare – by being able to better capture and analyze unstructured data, one health system is using this data to help identify patients with risks for chronic diseases to improve treatments and reduce readmission.

Fitness – the rise of mobile and wearables has led to a fitness app that tracks your performance and progress and coaches you through your workouts to meet fitness goals.

Retail – a clothing retailer has used big data to drive a better in-store experience using sensors and Wi-Fi data to track customer behavior while in the store.

Travel – one airline is using mobile to access customer preferences, travel history and even allergies to provide more personalized service in-flight.

In each of these examples, cognitive computing technology allows companies to harness big data to create better customer service experiences.

AI – Practical Applications

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Last week’s blog post featured information on what augmented intelligence (AI) means and the industries using the technology from IBM Research.  Today, let’s review a couple real life examples of AI applications.

First, a quick definition, IBM prefers to refer to AI as augmented intelligence.  Their approach is to use cognitive computing capabilities, such as machine learning, reasoning and decision tech, language, speech and visual tech and human interface tech, to create practical applications that enhance and scale human expertise.

IBM’s Watson Health – Partnering with New York’s Memorial Sloan Kettering Cancer Center (MSK), IBM’s application helps to consume and process the massive amounts of medical research while also “learning” from cancer experts, working to ultimately expand access to cancer treatment options and expertise. Laura Lorenzetti of Fortune explains, “Some MSK oncologists have a highly specific expertise in certain cancers. By training Watson to think like they do, that knowledge expands from one specialist to any doctor who is querying Watson. That means that a patient can get the same top-tier care as if they traveled directly to the center’s offices in Manhattan. IBM’s Watson provides the framework to learn, connect, and store the data, while MSK is imparting its knowledge to train the computer.”

Financial Services – cognitive computing is assisting financial advisors so  they can better serve their  clients.  By ingesting financial information and client data, Watson can answer the everyday client questions while also using its processing power to help identify potential options for the advisor to evaluate.  Many believe that by integrating with Watson financial advisors will be able to expand their practices and serve more clients. William Sprouse of Financial Planning further explains, “In practice, such cognitive computing power would work with an adviser just like a helpful Star Wars droid: virtually present during a meeting with a client, gathering data, and ready to instantly assist with queries and projections, along with its own suggestions based on client data.”

These two examples both demonstrate not only the processing power of augmented intelligence systems like Watson but also the ability to “learn”.  This ability to learn can provide access to critical expertise to more people than ever before in healthcare and financial services.

IBM: AI 101

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A recent article from Tech Crunch by Devin Coldewey highlights an RFI response from IBM regarding artificial intelligence (AI). Mr. Coldewey writes, “The field of artificial intelligence is so huge, and the potential applications so numerous, that it would be folly to try to explain it all in one — no, wait, IBM just did.” Today we will look at some of the highlights from IBM’s response.

First, artificial intelligence vs. augmented intelligence. IBM prefers to speak to augmented intelligence which is the process of creating systems that enhance and scale human expertise rather than systems that attempt to replicate human intelligence.  IBM further describes their approach as cognitive computing or “a comprehensive set of capabilities based on technologies such as machine learning, reasoning and decision technologies; language, speech and vision technologies; human interface technologies; distributed and high-performance computing; and new computing architectures and devices. When purposefully integrated, these capabilities are designed to solve a wide range of practical problems, boost productivity, and foster new discoveries across many industries.”

How is AI currently being used?  IBM provides the follow highlights by industry:

  • Healthcare – AI is advancing precision medicine through its ability to “ingest” patient information and run it against vast stores of medical research
  • Social Services – AI can be used to predict resource needs from specific population groups
  • Education – AI provides new capabilities to design true personalized learning plans
  • Financial Services – AI is being used to ensure financial resources are utilized well. This can come from the advancement of the applicant approval process or through efficient weighing and processing of insurance needs against risk, costs and regulations

In particular for IBM what started as a contestant on Jeopardy, IBM Watson, is now full blown cognitive computing that can be applied to practical problems in a variety of industries.

Next week, we will feature more on the blog from IBM’s AI 101. Be sure to check back. Until then you can also read more on AI and cognitive computing here.

The Olympics in the Cloud

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With the 2016 Olympics in Rio fast approaching, today, let’s take a look at how cloud computing  is impacting athletes around the world and the fans who cheer them on.

First, staying steady is everything when it comes to archery and this summer fans will be able to see in real-time the archer’s heart rate as they take aim, according to the official news outlet for the Rio 2016 Games.  Wearables used for payment will also be part of Olympic venues. Visa is working with a Brazilian bank on not only a bracelet for fans but also a “payment ring” which will be given and used by 45 sponsored athletes. Finally, to support the technology needs of the games, the official IT partner of the Olympic games has been working to migrate many of its operations to the cloud to reduce its hardware needs.  For example, they expect to have 250 servers for Rio down from 719 servers used during the London 2012 games.

Laura Gargolinski for IBM’s Thoughts on Cloud writes, “even more interesting is the way cloud technology is revolutionizing the way athletes (whether they are Rio-bound, or just regular people like you and me) eat, sleep, and train to improve their overall health, eliminate injury, and achieve optimum performance.”  In her article she highlights Team USA Cycling which has developed an application that provides real-time data analytics to cyclists while they workout or train.  IBM’s application allows the cyclist to make “on-the-spot” adjustments so they can optimize performance.  Even “regular” athletes as she describes herself, an avid runner, can take advantage of cloud-powered apps that track and help plan your training regime.

Cloud technology not only supports apps and wearables that allow athletes to perform better but it also opens up new experiences for fans and helps to make supporting tech operations more efficient.

IBM Shares The 3 “Cs” of Big Data

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There are the “Vs” of big data: volume, variety, velocity and veracity, but also Chris Nott of IBM in a recent article introduces us to the three “Cs” of big data. What are they? Confidence, context and choice. Today let’s look at his explanations on what each of the “Cs” mean.

Confidence.  Big data comes from many sources and most of the time to be useful it must be combined. Combining data is not an exact science.  There can be different data formats, definitions and variations in which the data is managed or stored. But, Mr. Nott points out a “confidence level” can be assigned so that leaders making decisions with the data can judge the quality and risk associated with the results. He adds, “the level of confidence that is acceptable is a judgment that a business needs to make based on the risk and effect of actions. And that judgment is a balance between what might result from poor decisions that arise from inaccurate data and the cost of making improvements in the provision of data.”

Context.  Big data grows fast and moves fast within an organization. Context is important not only for delivering the right information to the right person but also for granting the right access to the right person. Mr. Nott writes, “Understanding context requires understanding who is asking the question and why. And part of that grasp includes the role of the person, where that person is asking the question, what the questioner is trying to do and the purpose to which the results will be applied.”

Choice. There are a variety of tools and platforms available to crunch big data. IT must examine each tool and determine if it fits the purpose and needs of the business user. This “choice” is not one-size-fits-all and should be weighed against each groups’ needs as well as the organization’s governance policies so users have confidence in the platform choice that is offered.

The 3 “Cs” of big data help users develop a trust level with the data by first allowing them to understand the risks (confidence), then knowing the data is being delivered with their needs in mind (context) and finally being confident that they are utilizing a reliable set of tools.

3C's of Big Data - IBM
source: ibmbigdatahub.com

What’s Next for Big Data in Healthcare

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“Big Data will leave no sector untouched as it continues to change the way we think about everything from sales to human resources, and medicine and healthcare are no different,” writes Bernard Marr for Forbes.

Handling data is nothing new to the healthcare industry.  But, in recent years, increased abilities to share and access data plus new data from sensors and wearables has created not only more data but better data according to Mr. Marr.  In his article he outlines several ways big data will make an impact:

Prevention.  Mr. Marr writes, “smartphones and other popular smart devices including Jawbone, Fitbit and others, now have the capacity to help people track their progress towards a healthier lifestyle. Apps and devices to help track and monitor physical fitness but also chronic ailments like diabetes, Parkinson’s and heart disease are also being developed.”  Not only do these devices track more data but this data can be more reliable than traditional patient led tracking and reporting methods.

Diagnosis. Improvements are being made to how big data is stored and shared in the healthcare industry with the goal of  bringing medical providers more access. Systems, such as IBM’s Watson, are also looking at test results, recognizing patterns and learning in order to aid in diagnostics and improve early detection.

Treatment.  To get to more personalized medicine and better patient  treatments you first have to start big, as in big data.  The results of big data analytics, predictive modeling and new systems crunching vast amounts of information will help to better inform doctors about the needs of each patient.

Big data is definitely a “game changer” for the healthcare industry both now and in the future.